Video surveillance is a key technology for enhanced protection of facilities such as airports and power stations. Video surveillance hardware has developed to the point where the implementation of networks having thousands of cameras is now feasible. However, constructing software that efficiently and reliably deals with networks of this size remains a problem.
A key step towards automating surveillance of video from many cameras is to generate an understanding of the paths which targets take between the field of views of different cameras. Detecting a threat based on the historical path information requires finding the correlation between the flows cross multiple cameras. Without such correlation, an abnormal threat behavior of a person visiting different surveillance zone may appear normal in each camera. This disclosure proposes to solve the problem using a novel activity topology discovery method to calculate correlation of statistical properties of object path between the entry and exit regions of the multiple cameras by using a decentralized approach in which the correspondence between cameras is carried out through message exchange. Each camera learns their source (entry) and sink (exit) regions, thereby reducing the state space. The space is further reduced by considering only the source and sink regions when determining correspondence between cameras. Learned topology information may be also used to answer alarm related queries and combine surveillance video recordings from multiple cameras into a coherent retrieval result.
Learned correlation between the object paths between cameras forms a normal activity topology information base that may be used to detect the threat level of object traveling from camera to camera. It can also be used to display highly correlated surveillance video in adjacent position to facilitate tracking a fast moving objects cross multiple cameras.
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.